Abstract Sensoriis, Inc is a company that, develops evidence-based sensing solutions to support biological investigations and address health care problems. The goal of this NIH STTR grant with University of Washington (UW) is to provide a flexible system to assess cardiac electrical activities in zebrafish models, supporting heart disease studies and drug screening. Unlike humans, zebrafish hearts can fully regenerate following cardiac injury, thereby providing a tractable model system to study endogenous heart regeneration. Zebrafish have also proven to be an ideal vertebrate model system for phenotype-based screening owing to their physiological similarity to mammals. Further, zebrafish model enables a forward genetic approach to reveal the genetic basis and underlying molecular mechanisms of numerous heart diseases. The conventional setup for cardiac phenotype acquisition in zebrafish (i.e. electrocardiogram ? ECG) involves sedation causing variation in functionality. To date, there is no system which can offer cardiac phenotype monitoring in freely-swimming zebrafish, not to mention for multiple fish simultaneously. In this context, we propose and develop 1) a wireless flexible ?jacket? to be worn by zebrafish for real-time assessment of electrical cardiac phenotypes, namely ECG; and 2) a simple-yet-novel apparatus to collect ECG of multiple awake fish. Our devices provide pivotal platforms for cardiac phenotype-related investigations. The obtained data will be processed by smart algorithms to detect aberrant ECG patterns in real time. The proposed systems will facilitate related studies using zebrafish models. Further, the success of this platform also paves the avenue for regenerative medicine and developmental biology studies as well as stem cell-based therapies for cardiac repair. In Phase I of this STTR grant, we will develop i) a polymer-based microelectrode array (MEA) jacket that could be comfortably worn by the zebrafish and provide wireless ECG acquisition; and ii) a 4-chamber apparatus for simultaneous recording of ECG in awake fish. Machine learning-based programs with embedded algorithms will be developed to distinguish ECG patterns such as heart rate, ST and QT intervals, thus can identify anomalies, such as arrhythmias or prolonged QTs. For proof of concept, the system will be validated and compared.